MFSO Tinkering
Phase 1 Kappa starting point: use value of 10 t/km^2 from McCormack et al. 20xx to establish a steady model. Match model to time-averaged fishing (catch)
Phase 2 Then use historical fishMIP values to estimate the profile of the phytoplakton size spectrum
ACEAS data: Can we get empirical phytoplankton or zooplankton size spectra (BCG ARGO floats or particle counters) Jase Everett or ACEAS folks
Using pico (6.94559e-11) and large (6.06131e-07) phyto midpoints from Phoebe’s method as the assumed phytoplankton size range. ‘Spread’ the biomass estimate from McCormack of 10 t/km^2 across that size range and using Barnes equation to estimate the density at the intercept (density per gram at 0 on log scale): kappa = 17.9
Kappa for the whole model domain is 17.9 * 1.474341e+12 = 2.63907e+13
Setting kappa: How do we want to express the model domain. m^2 or the entire domain volume? Banzare Bank model domain is 1.474341e+12 [m^2]
Determine the size range for the phytoplankton from Stacey’s model - Make sure it’s density per
Can use historical fishMIP values to estimate the kappa value
https://github.com/pwoodworth-jefcoats/therMizer-FishMIP-2022-HI/blob/main/ClimateForcing/Plankton/Prep_Plankton_therMizer.Rmd * Double check all units to make sure they match * Size classes hard-coded so make sure to edit * time steps also hard-coded (need to adjust the arrays)
To get the starting point for Phoebe’s script, need a timeseries of total carbon density
Steady state is the base model (null hypothesis..) that fulfills the following criteria. It allows us to investigate change relative to the base model.
Clear rule for the model to represent endotherms
Within 25% biomass estimates
Realised PPMRs are consistent with empirical knowledge
Ontogenetic shifts
Unexploited size spectrum that is within ‘x’ of the expected by theory (definitely negative)
Sheldon biomass distribution = approx. 0
Emergent versus constrained
Production to biomass ratios - Ecopath estimates
trophic level comparison with ecopath models
Borrowing from other models, piecing the evidence together to allow us to explore the S&F of ecosystem with plankton to whales.
KEY QUESTIONS:
What are the emergent ecosystem properties, productivity, resilience etc,
How sensitive are ecosystem properties to perturbation of uncertain parameters (mesopelagic biomass, kappa etc)
Load libraries
remotes::install_github("sizespectrum/mizerExperimental")Skipping install of 'mizerExperimental' from a github remote, the SHA1 (bbbf0828) has not changed since last install.
Use `force = TRUE` to force installationlibrary(mizerExperimental)
# remotes::install_github("sizespectrum/mizerMR")
library(mizer)
# library(mizerMR)
library(tidyverse)
library(plotly)
# library(lhs)Load group parameters
h values from data for some groups. Can try developing a
model with them intitally, but it may require deleting all
h values and proceeding from the beginning again with
default caluclations from mizer using k_vb
k_vb_string <- c(0.4,0.3608333,0.1825,0.15,1,0.2,0.5,0.06,0.2,0.2,0.2,0.2,0.2,0.2,0.2) # to use all k_vb values and instead of h
groups_raw <- read.csv("group params/trait_groups_params_vCWC.csv")
groups_no_h <- groups_raw[,-7]
# groups_no_h$biomass_observed
groups <- groups_no_h
k_vb_string <- c(0.4,0.3608333,0.1825,0.15,1,0.2,0.5,0.06,0.2,0.2,0.2,0.2,0.2,0.2,0.2) # to use all k_vb values and instead of h
groups_raw$k_vb <- k_vb_string
groups$k_vb <- k_vb_string
# species_params <- groups
# species_params[6,4] #max size for small divers
# species_params[6,3] #maturation size
# species_params[6,2] #minimum size
#
# species_params[6,3] <- 0.85*species_params[6,4]
# species_params[9,3] <- 0.99*species_params[9,4]
#
# groups_raw[6,3] <- 0.85*groups_raw[6,4]
# groups_raw[9,3] <- 0.99*groups_raw[9,4]
groupsRun a range of models to different biomass estimates Use an ensemble approach Perturbation to test the change in size structure and functions
Load interaction matrix
theta <- readRDS("interaction matrix/trait_groups_interaction_matrix_vCWC.RDS")
thetatheta[13,13][1] 1theta[13,13] <- 0
theta[13,13][1] 0Starting with empirically derived h values for some
groups Create param object
params_h_default <- newMultispeciesParams(species_params = groups,
interaction = theta,
kappa = 2.63907e+13,
w_pp_cutoff = 10000,
n = 3/4, p = 3/4)For the species leopard seals, large divers, minke whales, sperm whales the value for `w_mat` is not smaller than that of `w_max`. I have corrected that by setting it to about 25% of `w_mat.
For the species small divers, leopard seals the value for `w_min` is not smaller than that of `w_mat`. I have reduced the values.
No h provided for some species, so using age at maturity to calculate it.
Because the age at maturity is not known, I need to fall back to using von Bertalanffy parameters, where available, and
this is not reliable.
No ks column so calculating from critical feeding level.
Using z0 = z0pre * w_max ^ z0exp for missing z0 values.
Using f0, h, lambda, kappa and the predation kernel to calculate gamma.# params <- newMultispeciesParams(species_params = groups_raw,
# interaction = theta,
# w_pp_cutoff = 1,
# n = 3/4, p = 3/4)
get_h_default(params_h_default) [1] 9.436809 22.231901 12.707084 18.972280 48.143236 11.809790 128.200831 17.996374 56.988643 46.770049 25.155954 24.023236
[13] 25.150598 37.728272 97.635963# get_h_default(params)
# params_h_default@species_params$h - params@species_params$hbox.params <- params_h_default
box.params@species_params$ppmr_min[box.params@species_params$species == "baleen whales"] <- 1e5
box.params@species_params$ppmr_max[box.params@species_params$species == "baleen whales"] <-5e7
box.params@species_params$pred_kernel_type[box.params@species_params$species == "baleen whales"] <- "box"params_v1 <- box.params
params_v1@species_params$w_mat[params_v1@species_params$species == "leopard seals"] <- 3.480000e+05 * 0.9
params_v1@species_params$w_mat[params_v1@species_params$species == "large divers"] <- 2.024000e+06 * 0.9
params_v1@species_params$w_mat[params_v1@species_params$species == "minke whales"] <- 6.000000e+06 * 0.9
params_v1@species_params$w_mat[params_v1@species_params$species == "sperm whales"] <- 3.650000e+07 * 0.9
params_v1 <- setParams(params_v1)params_v2 <- params_v1
params_v2@species_params$w_min[params_v2@species_params$species == "small divers"] <- params_v2@species_params$w_mat[params_v2@species_params$species == "small divers"] * 0.85
params_v2@species_params$w_min[params_v2@species_params$species == "leopard seals"] <- params_v2@species_params$w_mat[params_v2@species_params$species == "leopard seals"] * 0.85
params_v2 <- setParams(params_v2)First simulation using raw param values Need to adjust starting Rmax values. Use Julia’s quick calibration using kappa (although starting kappa is a total guess as well)
params_guessed <- params_v2
params_guessed@species_params$R_max <- params_guessed@resource_params$kappa*params_guessed@species_params$w_max^-1.5
params_guessed <- setParams(params_guessed)
sim_guessed <- project(params_guessed, effort=0)
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plotlyFeedingLevel(sim_guessed)plotlyBiomass(sim_guessed)# plotDiet(sim_guessed)
plotSpectra(sim_guessed)
Apex predators have the most diverse diet and are only preying upon
dynamic groups. However, this means they don’t have enough to eat. We
can either open up resources available to them, although the empirical
observations suggest that they should be eating the larger inviduals of
groups like divers. So, instead of increasing beta or
increasing the width of their feeding kernel, we will try to add a new
resource spectrum that only apex predators can access to aid their
growth and reproduction. If we can reach a steady state in this fashion,
then we can remove the additional resource with further calibration
efforts.
plotDiet(params_guessed, species = "baleen whales")Error: 'plotDataFrame' is not an exported object from 'namespace:mizerExperimental'params_guessed@species_params$species [1] "euphausiids" "mesopelagic fishes" "bathypelagic fishes" "shelf and coastal fishes"
[5] "flying birds" "small divers" "squids" "toothfishes"
[9] "leopard seals" "medium divers" "large divers" "minke whales"
[13] "orca" "sperm whales" "baleen whales" params_guessed@species_params$R_max [1] 4.693004e+12 7.097968e+09 1.779177e+09 2.213787e+08 9.726029e+07 5.678375e+07 8.849487e+06 4.219330e+05 1.285529e+05 1.815979e+04
[11] 9.165057e+03 1.795660e+03 1.795660e+03 1.196773e+02 2.524615e+01params_v1 <- params_guessed
# params_v1@species_params$R_max[params_v1@species_params$species == "orca"] <- Inf
params_v1@species_params$gamma[params_v1@species_params$species == "orca"] <- params_v1@species_params$gamma[params_v1@species_params$species == "orca"] * 50
params_v1 <- setParams(params_v1)
sim_guessed <- project(params_v1, effort=0, t_max = 1000)
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Refine box feeding kernel for the baleen whales max ppmr value are based on w_max baleen whale feeding on w_min euphausiids min ppmr value based on w_min baleen whale feeding on w_max euphausiids
params_v2 <- params_v1
params_v2@species_params$w_min[params_v2@species_params$species == "baleen whales"][1] 2250000params_v2@species_params$w_max[params_v2@species_params$species == "baleen whales"][1] 1.03e+08params_v2@species_params$w_min[params_v2@species_params$species == "euphausiids"][1] 6.31e-05params_v2@species_params$w_max[params_v2@species_params$species == "euphausiids"][1] 3.162278# 1.03e+08/6.31e-05 # w_max baleen whale divided by w_min euphausiids
# 2250000/3.162278 # w_min baleen whale divided by w_max euphausiids
params_v2@species_params$ppmr_min[params_v2@species_params$species == "baleen whales"] <- 711512.4
params_v2@species_params$ppmr_max[params_v2@species_params$species == "baleen whales"] <- 1.63233e+12
# params_v2@species_params$ppmr_min[params_v2@species_params$species == "baleen whales"] <- 1e5
# params_v2@species_params$ppmr_max[params_v2@species_params$species == "baleen whales"] <-5e7
params_v2@species_params$pred_kernel_type[params_v2@species_params$species == "baleen whales"] <- "box"
params_v2 <- setParams(params_v2)
sim_v2 <- project(params_v2, effort=0, t_max = 1000)
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[===========================================>] 99% ETA: 0s
[===========================================>] 100% ETA: 0splot(sim_v2)
Now I will try to reduce the max size of the resource
params_v3@resource_params$w_pp_cutoff
[1] 100params_v3 <- setParams(params_v3)
sim_v3 <- project(params_v3, effort=0, t_max = 1000)
[--------------------------------------------] 1% ETA: 41s
[>-------------------------------------------] 1% ETA: 38s
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[==========================================>-] 97% ETA: 1s
[==========================================>-] 97% ETA: 0s
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[==========================================>-] 99% ETA: 0s
[===========================================>] 99% ETA: 0s
[===========================================>] 100% ETA: 0splot(sim_v3)
params_v3_steady <- steady(params_v3)
params_v3_tuned <- tuneParams(params_v3_steady)params_v3_tuned@resource_params$w_pp_cutoff
[1] 100
sim_v5 <- project(params_v5, effort=0, t_max = 1000)
[>-------------------------------------------] 1% ETA: 15s
[>-------------------------------------------] 2% ETA: 15s
[>-------------------------------------------] 2% ETA: 14s
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[==========================================>-] 97% ETA: 1s
[==========================================>-] 97% ETA: 0s
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[===========================================>] 99% ETA: 0s
[===========================================>] 100% ETA: 0splot(sim_v5)
plotlyBiomass(sim_v5)params_v5_steady <- steady(params_v5)
params_v5_tuned <- tuneParams(params_v5_steady)# params_v6 <- setParams(params_v5_tuned)
sim_v6 <- project(params_v5_tuned, effort=0, t_max = 2000)
[--------------------------------------------] 1% ETA: 32s
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[>-------------------------------------------] 1% ETA: 1m
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plotlyBiomass(sim_v6)
params_v7 <- params_v6 |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady()Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
params_v8 <- tuneGrowth(params_v7)
Listening on http://127.0.0.1:6336
params_v9 <- steady(params_v8, t_max = 1000)Convergence was achieved in 1.5 years.sim_v8 <- project(params_v9, effort=0, t_max = 1000)
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plotlyBiomass(sim_v8)params_v9@species_params$erepro [1] 0.006539088 0.008324580 0.017341896 0.011144187 0.003926176 0.235560686 0.010509550 0.052390073 0.205232541 0.020714097 0.026345845
[12] 0.020721349 0.051218847 0.028016399 0.024934169
kappa <- resource_params(cm_v1)$kappa
lambda <- resource_params(cm_v1)$lambda
w_max <- 100
w_full(cm_v1)[[1]][1] 8.033057e-13w_min <- 1e-12Now we put all these resource parameters into a data frame.
SO_resource_params <- data.frame(
resource = c("pl"),
lambda = c(lambda),
kappa = c(kappa),
w_min = c(w_min),
w_max = c(w_max)
)
SO_resource_paramsparams_pl_rs <- cm_v1
resource_params(params_pl_rs) <- SO_resource_params# params_pparams_pl_rsl_rs <- setParams(params_pl_rs)
sim_v_pl_rs <- project(params_pl_rs, effort=0, t_max = 1000)
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mizer::plotDiet(params_pl_rs)
mizer::plotDiet(params_v4)params <- readRDS("tuned_params.rds")
params <- steady(params)
Check the spectra
plotlySpectra(params, power = 2)plotBiomassVsSpecies(params)params <- calibrateBiomass(params)
plotBiomassVsSpecies(params)
plotlySpectra(params)Rescale species spectra
params <- matchBiomasses(params)
plotBiomassVsSpecies(params)Can add upper and lower boundaries for observed biomass range
params_v3 <- tuneParams(params)plotlySpectra(params_v3)
sim <- project(params_v3, effort = 0 , t_max = 500)
plot(sim)params_v4 <- tuneParams(params_v3)
params_v5 <- tuneGrowth(params_v4)
params_v6 <- tuneParams(params_v5)
# saveRDS(params_v6, "phase1_steady.rds")Check mizerHowTo Customise the match biomass plots to compare the correct ranges
params <- readRDS("phase1_steady.rds")Check biomass is at steady-state
sim <- project(params, effort = 0)
plotlyBiomass(sim)
plotlyBiomassObservedVsModel(sim)Check diets
mizer::plotDiet(params)try to reduce the maximum size of the background resource to force predation among dynamics groups
params_wpp_v1 <- setParams(params, w_pp_cutoff = 100)
params_wpp_v1 <- setResource(params, w_pp_cutoff = 100)params_wpp_v2 <- steady(params_wpp_v1)params_wpp_v3 <- tuneParams(params_wpp_v2)sim_v2 <- project(params_wpp_v1, t_max = 500)
plot(sim_v2)mizer::plotDiet(params_wpp_v1)params_guessed_v2 <- params_guessed
params_guessed_v2@species_params$beta[params_guessed_v2@species_params$species == "orca"] <- 100Something like ‘yieldCatch’
params_guessed_v2 <- setParams(params_guessed_v2)
sim_v2 <- project(params_guessed_v2, effort=0)
plot(sim_v2)
plotlyFeedingLevel(sim_v2)
plotlyBiomass(sim_v2)params_v3 <- setParams(params_guessed_v2, w_pp_cutoff = 100000)
sim_v3 <- project(params_v3, effort=0)
plot(sim_v3)
plotlyFeedingLevel(sim_v3)
plotlyBiomass(sim_v3)theta_v2 <- theta
theta_v2[6,] # small divers
theta_v2[9,] # leopard seals
theta_v2[13,] # orca
theta_v2[6,c(1:4,7,8)] <- 0.5 # small divers
theta_v2[c(1:4,7,8), 6] <- 0.5
theta_v2[9, c(1:4,7,8)] <- 0.5
theta_v2[c(1:4,7,8), 9] <- 0.5
# theta_v2[13, c(1:4,7,8)]
# theta_v2[13, c(1:4,7,8)]
theta_v2params_theta_v2 <- newMultispeciesParams(species_params = species_params,
interaction = theta_v2,
w_pp_cutoff = 100000,
n = 3/4, p = 3/4)
box.params_v2 <- params_theta_v2
box.params_v2@species_params$ppmr_min[box.params_v2@species_params$species == "baleen whales"] <- 1e5
box.params_v2@species_params$ppmr_max[box.params_v2@species_params$species == "baleen whales"] <-5e7
box.params_v2@species_params$pred_kernel_type[box.params_v2@species_params$species == "baleen whales"] <- "box"params_v2 <- box.params_v2
params_v2@species_params$R_max <-params_v2@resource_params$kappa*params_v2@species_params$w_inf^-1.5
params_v2 <- setParams(params_v2)
sim_v2 <- project(params_v2, effort=0)
plot(sim_v2)
plotlyFeedingLevel(sim_v2)
plotlyBiomass(sim_v2)
# plotDiet(sim_v2)plotSpectra(sim_v2, power = 2)params_tuned_v1 <- tuneGrowth(params_guessed)library(mizerMR)
resource_interaction(params_guessed)
#set vectors of plankton and benthos availability for the model species
plankton_avail <- c(1, 0.8, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)
# benthos_avail <- c(0.2, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5)
#put them into corresponding columns of resource_interaction matrix
resource_interaction(sim_guessed)[, 1] <- plankton_avail
resource_interaction(cur_model)[, 2] <- benthos_avail# plotBiomassVsSpecies(params_guessed)
# params_v0 <- calibrateBiomass(params_guessed)
# params_v0 <- matchBiomasses(params_guessed)
# resource_params(params_guessed)# kappa <- resource_params(params_guessed)$kappa
# lambda <- resource_params(params_guessed)$lambda
#
# w_max <- 10
#
# w_full(params_guessed)[[1]]# w_min <- 1e-12Setup apex predator resource
# # Set ap_rss kappa same as plankton kappa
# kappa_ap <- kappa
#
# # Assume more shallow slope for benthos
# lambda_ap <- lambda
# # Set maximum ap prey size
# w_max_ap <- 10000000
# # Set minimum ap prey size
# w_min_ap <- 1000# # Set benthos kappa same as plankton kappa
# kappa_ben <- kappa
# # Assume more shallow slope for benthos
# lambda_ben <- 1.9
# # Set maximum benthos size
# w_max_ben <- 10
# # Benthos starts at larger sizes, corresponding to about 1-2mm
# w_min_ben <- 0.0001Now we put all these resource parameters into a data frame.
# MFSO_resource_params <- data.frame(
# resource = c("pl", "ap"),
# lambda = c(lambda, lambda_ap),
# kappa = c(kappa, kappa_ap),
# w_min = c(w_min, w_min_ap),
# w_max = c(w_max, w_max_ap)
# )
#
# MFSO_resource_paramsWe can now update our model to use these resource parameters with
# resource_params(params_guessed) <- MFSO_resource_params# resource_interaction(params_guessed)# #set vectors of plankton and benthos availability for the model species
# plankton_avail <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.25)
# ap_avail <- c(0, 0, 0, 0, 0, 0, 0, 0, 0.25, 0.75, 0.75, 0)
#
# #put them into corresponding columns of resource_interaction matrix
# resource_interaction(params_guessed)[, 1] <- plankton_avail
# resource_interaction(params_guessed)[, 2] <- ap_availConfirm it worked
# resource_interaction(params_guessed)Try steady for a laugh
MFSO_mod <- steady(params_guessed)Nah, didn’t think so…hang on a second!
plotlySpectra(MFSO_mod, power = 2)plotBiomassVsSpecies(MFSO_mod)MFSO_mod <- calibrateBiomass(MFSO_mod)
plotBiomassVsSpecies(MFSO_mod)MFSO_mod <- matchBiomasses(MFSO_mod)
plotBiomassVsSpecies(MFSO_mod)MFSO_mod <- steady(MFSO_mod)MFSO_mod <- steady(MFSO_mod)
plotBiomassVsSpecies(MFSO_mod)MFSO_mod <- MFSO_mod |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady()Increase kappa to help erepro values that are too large. newMultispecies to adjust kappa, or in tuneParams(), but you need to make sure that kappa is actually changing.
plotBiomassVsSpecies(MFSO_mod)
plotSpectra(MFSO_mod, power = 2)params_v2 <- MFSO_mod
# write_rds(params_v2, "tuned_params.RDS")
params_v2 <- setParams(params_v2)
sim_v2 <- project(params_v2, effort=0)
plot(sim_v2)
plotlySpectra(sim_v2)
# plotDiet(sim_v2)param_setup <- params_v2
param_setup@resource_params$kappa
param_setup@species_params$R_max[]
param_setup@species_params$erepro[]params_red_pp_v1 <- param_setup
str(resource_params(params_red_pp_v1))
# mizer::resource_params(params_red_pp_v1)$w_pp_cutoff <- 10
mizer::resource_params(params)[["w_pp_cutoff"]] <- 10
params_red_pp_v1 <- steady(params_red_pp_v1)MFSO_mod <- matchBiomasses(params_red_pp_v1)
plotBiomassVsSpecies(params_red_pp_v1)
mizerMR::plotlySpectra(param_setup)
mizerMR::plotlySpectra(params_red_pp_v1)
mizerMR::plotDiet(params_red_pp_v1)params_red_pp_v2 <- setParams(params_red_pp_v1, w_pp_cutoff = 10000)params_red_pp_v2 <- steady(params_red_pp_v2)MFSO_mod_v2 <- matchBiomasses(params_red_pp_v2)
plotBiomassVsSpecies(params_red_pp_v2)
mizerMR::plotlySpectra(params_red_pp_v1)
mizerMR::plotlySpectra(params_red_pp_v2)
plotFeedingLevel(params_red_pp_v2, include_critical = T)
mizerMR::plotDiet(params_red_pp_v2)params_red_pp_v3 <- setParams(params_red_pp_v2, w_pp_cutoff = 1000)params_red_pp_v3 <- steady(params_red_pp_v3)MFSO_mod_v3 <- matchBiomasses(params_red_pp_v3)
plotBiomassVsSpecies(params_red_pp_v3)
mizerMR::plotlySpectra(params_red_pp_v2)
mizerMR::plotlySpectra(params_red_pp_v3)
plotFeedingLevel(params_red_pp_v3, include_critical = T)
mizerMR::plotDiet(params_red_pp_v3)params_red_pp_v4 <- setParams(params_red_pp_v3, w_pp_cutoff = 100)params_red_pp_v4 <- steady(params_red_pp_v4)MFSO_mod_v4 <- matchBiomasses(params_red_pp_v4)
plotBiomassVsSpecies(params_red_pp_v4)
mizerMR::plotlySpectra(params_red_pp_v3)
mizerMR::plotlySpectra(params_red_pp_v4)
plotFeedingLevel(params_red_pp_v4, include_critical = T)
mizerMR::plotDiet(params_red_pp_v4)par_test <- param_setup
# par_test@resource_params$kappa <- 18.3593
# new_vary <- c(param_setup@species_params$R_max, 18.3593)
Rmax_vary <- c(par_test@species_params$R_max)
erepro_vary <- c(par_test@species_params$erepro)
bio_vary <- c(par_test@species_params$R_max, par_test@resource_params$kappa)
getError_Rmax(vary = Rmax_vary, params = par_test, dat = par_test@species_params$biomass_observed, data_type="biomass", timetorun = 100)
getError_erepro(vary = erepro_vary, params = par_test, dat = par_test@species_params$biomass_observed, data_type="biomass", timetorun = 100)
getError_Bio(vary = bio_vary, params = par_test, dat = par_test@species_params$biomass_observed, data_type="biomass", timetorun = 100)min(param_setup@species_params$R_max[])
max(param_setup@species_params$R_max[])Optimise
library(parallel)
#create a set of params for the optimisation process
params_optim <- par_test
params_optim <- setParams(params_optim)
#set up workers
noCores <- detectCores() - 2 # keep some spare cores
cl <- makeCluster(noCores, setup_timeout = 0.5)
setDefaultCluster(cl = cl)
clusterExport(cl, as.list(ls()))
clusterEvalQ(cl, {
library(mizerExperimental)
library(mizerMR)
library(optimParallel)
})
optim_result <- optimParallel::optimParallel(par=Rmax_vary,getError_Rmax,params=params_optim,
dat = params@species_params$biomass_observed,
data_type="biomass", timetorun = 100,
method ="L-BFGS-B",
lower=c(rep(1e-20,length(params@species_params$species))),
upper= c(rep(10,length(params@species_params$species))),
parallel=list(loginfo=TRUE, forward=TRUE))
stopCluster(cl)
params_optim@species_params$R_max
# params_optim@species_params$erepro
optim_result$par[1:12]
# optim_result$par[13]params_optim_v2 <- params_optim
# now put these new Rmaxs
# optim values:
params_optim_v2@species_params$R_max <- optim_result$par[1:12] # removed the 10^
# params_optim@species_params$erepro <- optim_result$par[1:12]
# params_optim@resource_params$kappa <- optim_result$par[13]
# vary_optim <- optim_result$par
# vary_optim[13] <- 1e3
#
# getErrorBio2(vary = vary_optim,
# params_optim, dat = params_optim@species_params$biomass_observed)
#check values ^^ have they gone to max/min etc.
params_optim_v2@species_params$max_lim <- 10
params_optim_v2@species_params$min_lim <- 1e-20
#
params_optim_v2@species_params$R_max > params_optim_v2@species_params$min_lim
params_optim_v2@species_params$R_max < params_optim_v2@species_params$max_lim
params_optim_v2 <-setParams(params_optim_v2)
sim_optim <- project(params_optim_v2, effort =0, t_max = 100)
plot(sim_optim)
plotlyBiomass(sim_optim)
plotDiet(sim_optim)Optimise again
params_optim_v2@resource_params$kappa params_optim_v3 <- steady(params_optim_v2)plotSpectra(params_optim_v3, power =2)params_optim_v4 <- tuneGrowth(params_optim_v3)
params_optim_v4 <- steady(params_optim_v4)params_optim_v4 <- readRDS("params_optim_v4.RDS")
sim_v3 <- project(params_optim_v4, effort =0, t_max = 100)
plotBiomassVsSpecies(params_optim_v4)
plotDiet(params_optim_v4)
plotBiomass(sim_v3)
plotlySpectra(sim_v3)
p1 <- plotSpectra(sim_v3)
# saveRDS(params_optim_v4, "params_optim_v4.RDS")
tiff(file="saving_spectra_plot.tiff",
width=6, height=4, units="in", res=500)
p1
dev.off()
If AAD recommend that benthic resource is not meaningful for toothfish
Rmax can be justified by applying a density dependence relationship according to the trait based model (Anderson) If finished with an optim run, could re-introduce density dependence that has been over written by steady()
We don’t know what the level of density dependence should be set at. Can use yield curves, sensitivity to mortality…harvesting
Pristine whale biomass pre-whaling. The ‘base’ model is actually the highly exploited system, can compare historical whaling time series (Chris Clements and Julia have this data)
Body size of whales responding to fishing - Follow
Empricial size distribs of mammals
yield
seaaroundus
We are considering the whales within the Prydz Bay
Gear and catch ability for krill, ice fish and toothfish
Combine Stacey/Roshni model and get a total biomass
params_optim_v4@species_params$erepro
params_optim_v5 <- tuneParams(params_optim_v4)
# params_optim_v5 <- tuneGrowth(params_optim_v4)
getReproductionLevel(params_optim_v4) # no density dependence for groups with 0
# Can try optimise with reproduction level
params_optim_v4@species_params$R_max
params_optim_v4@species_params$erepro
First ecosystem mizer model
# library(parallel)
# #create a set of params for the optimisation process
# # param_optim_v2
# param_optim_v2 <- setParams(param_optim_v2)
#
# #set up workers
# noCores <- detectCores() - 2 # keep some spare cores
# cl <- makeCluster(noCores, setup_timeout = 0.5)
# setDefaultCluster(cl = cl)
# clusterExport(cl, as.list(ls()))
# clusterEvalQ(cl, {
# library(mizerExperimental)
# library(mizerMR)
# library(optimParallel)
# })
#
# optim_result <- optimParallel::optimParallel(par=bio_vary,getError_Bio,params=params_optim,
# dat = params@species_params$biomass_observed,
# data_type="biomass", timetorun = 100,
# method ="L-BFGS-B",
# lower=c(rep(1e-20,length(params@species_params$species)),1),
# upper= c(rep(10,length(params@species_params$species)),1e+15),
# parallel=list(loginfo=TRUE, forward=TRUE))
# stopCluster(cl)Add Benthic Resource Spectrum
# Set benthos kappa same as plankton kappa
kappa_ben <- kappa
# Assume more shallow slope for benthos
lambda_ben <- 1.9
# Set maximum benthos size
w_max_ben <- 10
# Benthos starts at larger sizes, corresponding to about 1-2mm
w_min_ben <- 0.0001Now we put all these resource parameters into a data frame.
MFSO_resource_params_v2 <- data.frame(
resource = c("pl", "ap", "bb"),
lambda = c(lambda, lambda_ap, lambda_ben),
kappa = c(kappa, kappa_ap, kappa_ben),
w_min = c(w_min, w_min_ap, w_min_ben),
w_max = c(w_max, w_max_ap, w_max_ben)
)
MFSO_resource_params_v2params_bb <- params_optim_v4
#set vectors of plankton and benthos availability for the model species
plankton_avail <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.25)
ap_avail <- c(0, 0, 0, 0, 0, 0, 0, 0, 0.25, 0.75, 0.75, 0)
bb_avail <- c(0.1, 0, 0.25, 0.25, 0, 0.25, 0.25, 0.5, 0.25, 0, 0, 0)
#put them into corresponding columns of resource_interaction matrix
resource_interaction(params_bb)[, 1] <- plankton_avail
resource_interaction(params_bb)[, 2] <- ap_avail
resource_interaction(params_bb)[, 3] <- bb_avail
We can now update our model to use these resource parameters with
params_optim_v5 <- setMultipleResources(params_optim_v4, MFSO_resource_params_v2)
resource_params(params_optim_v4) <- MFSO_resource_params_v2resource_interaction(params_optim_v4)#set vectors of plankton and benthos availability for the model species
plankton_avail <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.25)
ap_avail <- c(0, 0, 0, 0, 0, 0, 0, 0, 0.25, 0.75, 0.75, 0)
#put them into corresponding columns of resource_interaction matrix
resource_interaction(params_guessed)[, 1] <- plankton_avail
resource_interaction(params_guessed)[, 2] <- ap_availConfirm it worked
resource_interaction(params_guessed)---
title: "MFSO Tinkering"
output: html_notebook
---
Phase 1
Kappa starting point: use value of 10 t/km^2 from McCormack et al. 20xx to establish a steady model.
Match model to time-averaged fishing (catch)

Phase 2
Then use historical fishMIP values to estimate the profile of the phytoplakton size spectrum

ACEAS data:
Can we get empirical phytoplankton or zooplankton size spectra (BCG ARGO floats or particle counters) Jase Everett or ACEAS folks

Using pico (6.94559e-11) and large (6.06131e-07)  phyto midpoints from Phoebe's method as the assumed phytoplankton size range. 'Spread' the biomass estimate from McCormack of 10 t/km^2 across that size range and using Barnes equation to estimate the density at the intercept (density per gram at 0 on log scale): kappa = 17.9 

Kappa for the whole model domain is 17.9 * 1.474341e+12 = 2.63907e+13

Setting kappa:
How do we want to express the model domain. m^2 or the entire domain volume?
Banzare Bank model domain is 1.474341e+12 [m^2]

Determine the size range for the phytoplankton from Stacey's model
- Make sure it's density per 

Can use historical fishMIP values to estimate the kappa value

https://github.com/pwoodworth-jefcoats/therMizer-FishMIP-2022-HI/blob/main/ClimateForcing/Plankton/Prep_Plankton_therMizer.Rmd
* Double check all units to make sure they match
* Size classes hard-coded so make sure to edit 
* time steps also hard-coded (need to adjust the arrays)

To get the starting point for Phoebe's script, need a timeseries of total carbon density

Steady state is the base model (null hypothesis..) that fulfills the following criteria.
It allows us to investigate change relative to the base model.

Clear rule for the model to represent endotherms

Within 25% biomass estimates

Realised PPMRs are consistent with empirical knowledge

Ontogenetic shifts

Unexploited size spectrum that is within 'x' of the expected by theory (definitely negative)

Sheldon biomass distribution = approx. 0

Emergent versus constrained

Production to biomass ratios - Ecopath estimates

trophic level comparison with ecopath models

Borrowing from other models, piecing the evidence together to allow us to explore the S&F of ecosystem with plankton to whales.

KEY QUESTIONS:

What are the emergent ecosystem properties, productivity, resilience etc, 

How sensitive are ecosystem properties to perturbation of uncertain parameters (mesopelagic biomass, kappa etc)


Load libraries 
```{r}
remotes::install_github("sizespectrum/mizerExperimental")
library(mizerExperimental)
# remotes::install_github("sizespectrum/mizerMR")
library(mizer)
# library(mizerMR)
library(tidyverse)
library(plotly)
# library(lhs)
```

Load group parameters

`h` values from data for some groups.
Can try developing a model with them intitally, but it may require deleting all `h` values and proceeding from the beginning again with default caluclations from mizer using `k_vb`

k_vb_string <- c(0.4,0.3608333,0.1825,0.15,1,0.2,0.5,0.06,0.2,0.2,0.2,0.2,0.2,0.2,0.2) # to use all k_vb values and instead of h

```{r}
groups_raw <- read.csv("group params/trait_groups_params_vCWC.csv")

groups_no_h <- groups_raw[,-7]

# groups_no_h$biomass_observed

groups <- groups_no_h

k_vb_string <- c(0.4,0.3608333,0.1825,0.15,1,0.2,0.5,0.06,0.2,0.2,0.2,0.2,0.2,0.2,0.2) # to use all k_vb values and instead of h

groups_raw$k_vb <- k_vb_string
groups$k_vb <- k_vb_string

# species_params <- groups

# species_params[6,4] #max size for small divers
# species_params[6,3] #maturation size
# species_params[6,2] #minimum size
# 
# species_params[6,3] <- 0.85*species_params[6,4]
# species_params[9,3] <- 0.99*species_params[9,4]
# 
# groups_raw[6,3] <- 0.85*groups_raw[6,4]
# groups_raw[9,3] <- 0.99*groups_raw[9,4]
groups
```

Run a range of models to different biomass estimates 
Use an ensemble approach
Perturbation to test the change in size structure and functions

Load interaction matrix
```{r}
theta <- readRDS("interaction matrix/trait_groups_interaction_matrix_vCWC.RDS")
theta
```

```{r}
theta[13,13]

theta[13,13] <- 0

theta[13,13]
```

Starting with empirically derived `h` values for some groups
Create `param` object

```{r}
params_h_default <- newMultispeciesParams(species_params = groups, 
                                interaction = theta, 
                                kappa = 2.63907e+13,
                                w_pp_cutoff = 10000,
                                n = 3/4, p = 3/4)

# params <- newMultispeciesParams(species_params = groups_raw,
#                                 interaction = theta,
#                                 w_pp_cutoff = 1,
#                                 n = 3/4, p = 3/4)

get_h_default(params_h_default)
# get_h_default(params)

# params_h_default@species_params$h - params@species_params$h
```

```{r}
box.params <- params_h_default

box.params@species_params$ppmr_min[box.params@species_params$species == "baleen whales"]  <- 1e5
box.params@species_params$ppmr_max[box.params@species_params$species == "baleen whales"] <-5e7
box.params@species_params$pred_kernel_type[box.params@species_params$species == "baleen whales"] <- "box"
```


```{r}
params_v1 <- box.params

params_v1@species_params$w_mat[params_v1@species_params$species == "leopard seals"]  <- 3.480000e+05 * 0.9
params_v1@species_params$w_mat[params_v1@species_params$species == "large divers"]  <- 2.024000e+06 * 0.9
params_v1@species_params$w_mat[params_v1@species_params$species == "minke whales"]  <- 6.000000e+06 * 0.9
params_v1@species_params$w_mat[params_v1@species_params$species == "sperm whales"]  <- 3.650000e+07 * 0.9

params_v1 <- setParams(params_v1)
```


```{r}
params_v2 <- params_v1

params_v2@species_params$w_min[params_v2@species_params$species == "small divers"]  <- params_v2@species_params$w_mat[params_v2@species_params$species == "small divers"] * 0.85
params_v2@species_params$w_min[params_v2@species_params$species == "leopard seals"]  <- params_v2@species_params$w_mat[params_v2@species_params$species == "leopard seals"] * 0.85

params_v2 <- setParams(params_v2)
```

First simulation using raw param values
Need to adjust starting Rmax values. Use Julia's quick calibration using kappa (although starting kappa is a total guess as well)



```{r}
params_guessed <- params_v2

params_guessed@species_params$R_max <- params_guessed@resource_params$kappa*params_guessed@species_params$w_max^-1.5

params_guessed <- setParams(params_guessed)

sim_guessed <- project(params_guessed, effort=0)

plot(sim_guessed)
plotlyFeedingLevel(sim_guessed)
plotlyBiomass(sim_guessed)
# plotDiet(sim_guessed)
plotSpectra(sim_guessed)
```
Apex predators have the most diverse diet and are only preying upon dynamic groups. However, this means they don't have enough to eat. We can either open up resources available to them, although the empirical observations suggest that they should be eating the larger inviduals of groups like divers. So, instead of increasing `beta` or increasing the width of their feeding kernel, we will try to add a new resource spectrum that only apex predators can access to aid their growth and reproduction. If we can reach a steady state in this fashion, then we can remove the additional resource with further calibration efforts.


```{r}
plotDiet(params_guessed, species = "baleen whales")
plotDiet(params_guessed, species = "orca")

```

```{r}
params_guessed@species_params$species
params_guessed@species_params$R_max


params_v1 <- params_guessed

# params_v1@species_params$R_max[params_v1@species_params$species == "orca"] <- Inf
params_v1@species_params$gamma[params_v1@species_params$species == "orca"] <- params_v1@species_params$gamma[params_v1@species_params$species == "orca"] * 50

```


```{r}
params_v1 <- setParams(params_v1)

sim_guessed <- project(params_v1, effort=0, t_max = 1000)

plot(sim_guessed)
```


Refine box feeding kernel for the baleen whales
max ppmr value are based on w_max baleen whale feeding on w_min euphausiids
min ppmr value based on w_min baleen whale feeding on w_max euphausiids

```{r}
params_v2 <- params_v1

params_v2@species_params$w_min[params_v2@species_params$species == "baleen whales"]
params_v2@species_params$w_max[params_v2@species_params$species == "baleen whales"]

params_v2@species_params$w_min[params_v2@species_params$species == "euphausiids"]
params_v2@species_params$w_max[params_v2@species_params$species == "euphausiids"]
```



```{r}
# 1.03e+08/6.31e-05 # w_max baleen whale divided by w_min euphausiids
# 2250000/3.162278 # w_min baleen whale divided by w_max euphausiids

params_v2@species_params$ppmr_min[params_v2@species_params$species == "baleen whales"]  <- 711512.4
params_v2@species_params$ppmr_max[params_v2@species_params$species == "baleen whales"] <- 1.63233e+12

# params_v2@species_params$ppmr_min[params_v2@species_params$species == "baleen whales"]  <- 1e5
# params_v2@species_params$ppmr_max[params_v2@species_params$species == "baleen whales"] <-5e7
params_v2@species_params$pred_kernel_type[params_v2@species_params$species == "baleen whales"] <- "box"

```


```{r}
params_v2 <- setParams(params_v2)

sim_v2 <- project(params_v2, effort=0, t_max = 1000)

plot(sim_v2)
```

```{r}
mizer::plotDiet(params_v2)
```
```{r}
# saveRDS(params_v2, "stage1_steady.rds") #stage 1 is with baleen whale box kernel, adjusted maturation sizes for most marine mammals and a background resource with a large max size
```

Now I will try to reduce the max size of the resource
```{r}
params_v3 <- params_v2

params_v3@resource_params$w_pp_cutoff # started with 10000

params_v3@resource_params$w_pp_cutoff  <- 100
```


```{r}
params_v3 <- setParams(params_v3)

sim_v3 <- project(params_v3, effort=0, t_max = 1000)

plot(sim_v3)
```
```{r}
mizer::plotDiet(params_v3)
```

```{r}
params_v3_steady <- steady(params_v3)
params_v3_tuned <- tuneParams(params_v3_steady)
```


```{r}
params_v3_tuned@species_params$erepro
params_v3_tuned@species_params$w_mat
params_v3_tuned@species_params$w_mat25
params_v3_tuned@resource_params$w_pp_cutoff
```

```{r}
mizer::plotDiet(params_v3_tuned)
```

```{r}
params_v4 <- tuneGrowth(params_v3_tuned)

sim_v4 <- project(params_v4, effort=0, t_max = 1000)

plot(sim_v4)
plotlyBiomass(sim_v4)
```


```{r}
params_v5 <- tuneGrowth(params_v4)

sim_v5 <- project(params_v5, effort=0, t_max = 1000)

plot(sim_v5)
plotlyBiomass(sim_v5)
```



```{r}
params_v5_steady <- steady(params_v5)
params_v5_tuned <- tuneParams(params_v5_steady)
```


```{r}
# params_v6 <- setParams(params_v5_tuned)

sim_v6 <- project(params_v5_tuned, effort=0, t_max = 2000)

plot(sim_v6)
plotlyBiomass(sim_v6)
```



```{r}
params_v6 <- steady(params_v5_tuned)

plotBiomassVsSpecies(params_v6)
plotlySpectra(params_v6, power = 2)

params_v6 <- calibrateBiomass(params_v6)
plotBiomassVsSpecies(params_v6)
```

```{r}
params_v7 <- params_v6 |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady()
```
```{r}
sim_v7 <- project(params_v7, effort=0, t_max = 1000)

plot(sim_v7)
plotlyBiomass(sim_v7)
mizer::plotDiet(params_v7)
```

```{r}
params_v8 <- tuneGrowth(params_v7)
```



```{r}
params_v9 <- steady(params_v8, t_max = 1000)
```


```{r}
sim_v8 <- project(params_v9, effort=0, t_max = 1000)

plot(sim_v8)
plotlyBiomass(sim_v8)
params_v9@species_params$erepro
```




```{r}
params_v9@species_params$erepro

cm <- params_v9
cm <- setBevertonHolt(cm, erepro = c(0.006539088, 0.008324580, 0.017341896, 0.011144187, 0.003926176, 0.235560686, 0.010509550, 0.052390073, 0.205232541, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2))
species_params(cm) |> select(erepro, R_max)

cm_v1 <- steady(cm)
```

```{r}
sim_cm_v1 <- project(cm_v1, effort=0, t_max = 2000)

plot(sim_cm_v1)
plotlyBiomass(sim_cm_v1)
plotlySpectra(sim_cm_v1, power=1)
```

```{r}

```



```{r}
kappa <-  resource_params(cm_v1)$kappa
lambda <- resource_params(cm_v1)$lambda
w_max <- 100 
w_full(cm_v1)[[1]]
w_min <- 1e-12
```




Now we put all these resource parameters into a data frame.

```{r}
SO_resource_params <-  data.frame(
    resource = c("pl"),
    lambda = c(lambda),
    kappa = c(kappa),
    w_min = c(w_min),
    w_max = c(w_max)
)

SO_resource_params
```


```{r}
params_pl_rs <- cm_v1
resource_params(params_pl_rs) <- SO_resource_params
```


```{r}
# params_pparams_pl_rsl_rs <- setParams(params_pl_rs)

sim_v_pl_rs <- project(params_pl_rs, effort=0, t_max = 1000)

plot(sim_v_pl_rs)
mizer::plotDiet(params_pl_rs)
```

```{r}
mizer::plotDiet(params_v4)
```


```{r}
params <- readRDS("tuned_params.rds")

params <- steady(params)

```

Check the spectra
```{r}
plotlySpectra(params, power = 2)
```

```{r}
plotBiomassVsSpecies(params)
```
```{r}
params <- calibrateBiomass(params)
plotBiomassVsSpecies(params)
plotlySpectra(params)
```
Rescale species spectra
```{r}
params <- matchBiomasses(params)
plotBiomassVsSpecies(params)
```
Can add upper and lower boundaries for observed biomass range
```{r}
params_v3 <- tuneParams(params)
```


```{r}
plotlySpectra(params_v3)

sim <- project(params_v3, effort = 0 , t_max = 500)

plot(sim)
```

```{r}
params_v4 <- tuneParams(params_v3)
params_v5 <- tuneGrowth(params_v4)
params_v6 <- tuneParams(params_v5)

# saveRDS(params_v6, "phase1_steady.rds")
```

Check mizerHowTo
Customise the match biomass plots to compare the correct ranges

```{r}
params <- readRDS("phase1_steady.rds")
```

Check biomass is at steady-state
```{r}
sim <- project(params, effort = 0)

plotlyBiomass(sim)
plotlyBiomassObservedVsModel(sim)
```

Check diets
```{r}
mizer::plotDiet(params)
```
try to reduce the maximum size of the background resource to force predation among dynamics groups
```{r}
params_wpp_v1 <- setParams(params, w_pp_cutoff = 100)

params_wpp_v1 <- setResource(params, w_pp_cutoff = 100)
```


```{r}
params_wpp_v2 <- steady(params_wpp_v1)
```

```{r}
params_wpp_v3 <- tuneParams(params_wpp_v2)
```


```{r}
sim_v2 <- project(params_wpp_v1, t_max = 500)
plot(sim_v2)
```
```{r}
mizer::plotDiet(params_wpp_v1)
```


```{r}
params_guessed_v2 <- params_guessed

params_guessed_v2@species_params$beta[params_guessed_v2@species_params$species == "orca"]  <- 100
```

Something like 'yieldCatch'

```{r}
params_guessed_v2 <- setParams(params_guessed_v2)

sim_v2 <- project(params_guessed_v2, effort=0)

plot(sim_v2)
plotlyFeedingLevel(sim_v2)
plotlyBiomass(sim_v2)
```


```{r}
params_v3 <- setParams(params_guessed_v2, w_pp_cutoff = 100000)

sim_v3 <- project(params_v3, effort=0)

plot(sim_v3)
plotlyFeedingLevel(sim_v3)
plotlyBiomass(sim_v3)
```


```{r}
theta_v2 <- theta

theta_v2[6,] # small divers
theta_v2[9,] # leopard seals
theta_v2[13,] # orca

theta_v2[6,c(1:4,7,8)] <- 0.5  # small divers
theta_v2[c(1:4,7,8), 6] <- 0.5
theta_v2[9, c(1:4,7,8)] <- 0.5
theta_v2[c(1:4,7,8), 9] <- 0.5
# theta_v2[13, c(1:4,7,8)] 
# theta_v2[13, c(1:4,7,8)]

theta_v2
```

```{r}
params_theta_v2 <- newMultispeciesParams(species_params = species_params, 
                                interaction = theta_v2, 
                                w_pp_cutoff = 100000,
                                n = 3/4, p = 3/4)

```

```{r}
box.params_v2 <- params_theta_v2

box.params_v2@species_params$ppmr_min[box.params_v2@species_params$species == "baleen whales"]  <- 1e5
box.params_v2@species_params$ppmr_max[box.params_v2@species_params$species == "baleen whales"] <-5e7
box.params_v2@species_params$pred_kernel_type[box.params_v2@species_params$species == "baleen whales"] <- "box"
```


```{r}
params_v2 <- box.params_v2

params_v2@species_params$R_max <-params_v2@resource_params$kappa*params_v2@species_params$w_inf^-1.5

params_v2 <- setParams(params_v2)

sim_v2 <- project(params_v2, effort=0)

plot(sim_v2)
plotlyFeedingLevel(sim_v2)
plotlyBiomass(sim_v2)
# plotDiet(sim_v2)
```

```{r}
plotSpectra(sim_v2, power = 2)
```


```{r}
params_tuned_v1 <- tuneGrowth(params_guessed)
```



```{r}
library(mizerMR)
resource_interaction(params_guessed)
#set vectors of plankton and benthos availability for the model species 
plankton_avail <- c(1, 0.8, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)
# benthos_avail <- c(0.2, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5)

#put them into corresponding columns of resource_interaction matrix
resource_interaction(sim_guessed)[, 1] <- plankton_avail
resource_interaction(cur_model)[, 2] <- benthos_avail
```



```{r}
# plotBiomassVsSpecies(params_guessed)

# params_v0 <- calibrateBiomass(params_guessed)
# params_v0 <- matchBiomasses(params_guessed)

```



```{r}
# resource_params(params_guessed)
```

```{r}
# kappa <-  resource_params(params_guessed)$kappa
# lambda <- resource_params(params_guessed)$lambda
# 
# w_max <- 10
# 
# w_full(params_guessed)[[1]]
```

```{r}
# w_min <- 1e-12
```




Setup apex predator resource
```{r}
# # Set ap_rss kappa same as plankton kappa 
# kappa_ap <- kappa
# 
# # Assume more shallow slope for benthos 
# lambda_ap <- lambda
# # Set maximum ap prey size
# w_max_ap <- 10000000
# # Set minimum ap prey size
# w_min_ap <- 1000
```


```{r}
# # Set benthos kappa same as plankton kappa 
# kappa_ben <- kappa
# # Assume more shallow slope for benthos 
# lambda_ben <- 1.9
# # Set maximum benthos size 
# w_max_ben <- 10
# # Benthos starts at larger sizes, corresponding to about 1-2mm
# w_min_ben <- 0.0001
```



Now we put all these resource parameters into a data frame.

```{r}
# MFSO_resource_params <- data.frame(
#     resource = c("pl", "ap"),
#     lambda = c(lambda, lambda_ap),
#     kappa = c(kappa,  kappa_ap),
#     w_min = c(w_min, w_min_ap),
#     w_max = c(w_max,  w_max_ap)
# )
# 
# MFSO_resource_params
```

We can now update our model to use these resource parameters with

```{r}
# resource_params(params_guessed) <- MFSO_resource_params
```


```{r}
# resource_interaction(params_guessed)
```



```{r}
# #set vectors of plankton and benthos availability for the model species 
# plankton_avail <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.25)
# ap_avail <- c(0, 0, 0, 0, 0, 0, 0, 0, 0.25, 0.75, 0.75, 0)
# 
# #put them into corresponding columns of resource_interaction matrix
# resource_interaction(params_guessed)[, 1] <- plankton_avail
# resource_interaction(params_guessed)[, 2] <- ap_avail
```

Confirm it worked
```{r}
# resource_interaction(params_guessed)
```

Try `steady` for a laugh
```{r}
MFSO_mod <- steady(params_guessed)
```

Nah, didn't think so...hang on a second! 

```{r}
plotlySpectra(MFSO_mod, power = 2)
```

```{r}
plotBiomassVsSpecies(MFSO_mod)
```

```{r}
MFSO_mod <- calibrateBiomass(MFSO_mod)
plotBiomassVsSpecies(MFSO_mod)
```

```{r}
MFSO_mod <- matchBiomasses(MFSO_mod)
plotBiomassVsSpecies(MFSO_mod)
```

```{r}
MFSO_mod <- steady(MFSO_mod)
```

```{r}
MFSO_mod <- steady(MFSO_mod)
plotBiomassVsSpecies(MFSO_mod)
```

```{r}
MFSO_mod <- MFSO_mod |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady()
```

Increase kappa to help erepro values that are too large.
newMultispecies to adjust kappa, or in tuneParams(), but you need to make sure that kappa is actually changing.

```{r}
plotBiomassVsSpecies(MFSO_mod)
plotSpectra(MFSO_mod, power = 2)
```


```{r}
params_v2 <- MFSO_mod

# write_rds(params_v2, "tuned_params.RDS")

params_v2 <- setParams(params_v2)

sim_v2 <- project(params_v2, effort=0)

plot(sim_v2)
plotlySpectra(sim_v2)
# plotDiet(sim_v2)
```


```{r}
param_setup <- params_v2
param_setup@resource_params$kappa

param_setup@species_params$R_max[]
param_setup@species_params$erepro[]
```

```{r}
params_red_pp_v1 <- param_setup

str(resource_params(params_red_pp_v1))
# mizer::resource_params(params_red_pp_v1)$w_pp_cutoff <- 10

mizer::resource_params(params)[["w_pp_cutoff"]] <- 10

```

```{r}
params_red_pp_v1 <- steady(params_red_pp_v1)
```

```{r}
MFSO_mod <- matchBiomasses(params_red_pp_v1)
plotBiomassVsSpecies(params_red_pp_v1)
mizerMR::plotlySpectra(param_setup)
mizerMR::plotlySpectra(params_red_pp_v1)
mizerMR::plotDiet(params_red_pp_v1)
```


```{r}
params_red_pp_v2 <- setParams(params_red_pp_v1, w_pp_cutoff = 10000)
```

```{r}
params_red_pp_v2 <- steady(params_red_pp_v2)
```


```{r}
MFSO_mod_v2 <- matchBiomasses(params_red_pp_v2)
plotBiomassVsSpecies(params_red_pp_v2)
mizerMR::plotlySpectra(params_red_pp_v1)
mizerMR::plotlySpectra(params_red_pp_v2)
plotFeedingLevel(params_red_pp_v2, include_critical = T)
mizerMR::plotDiet(params_red_pp_v2)
```

```{r}
params_red_pp_v3 <- setParams(params_red_pp_v2, w_pp_cutoff = 1000)
```

```{r}
params_red_pp_v3 <- steady(params_red_pp_v3)
```

```{r}
MFSO_mod_v3 <- matchBiomasses(params_red_pp_v3)
plotBiomassVsSpecies(params_red_pp_v3)
mizerMR::plotlySpectra(params_red_pp_v2)
mizerMR::plotlySpectra(params_red_pp_v3)
plotFeedingLevel(params_red_pp_v3, include_critical = T)
mizerMR::plotDiet(params_red_pp_v3)
```


```{r}
params_red_pp_v4 <- setParams(params_red_pp_v3, w_pp_cutoff = 100)
```

```{r}
params_red_pp_v4 <- steady(params_red_pp_v4)
```

```{r}
MFSO_mod_v4 <- matchBiomasses(params_red_pp_v4)
plotBiomassVsSpecies(params_red_pp_v4)
mizerMR::plotlySpectra(params_red_pp_v3)
mizerMR::plotlySpectra(params_red_pp_v4)
plotFeedingLevel(params_red_pp_v4, include_critical = T)
mizerMR::plotDiet(params_red_pp_v4)
```

```{r}
par_test <- param_setup
# par_test@resource_params$kappa <- 18.3593

# new_vary <- c(param_setup@species_params$R_max, 18.3593)
Rmax_vary <- c(par_test@species_params$R_max)
erepro_vary <- c(par_test@species_params$erepro)
bio_vary <- c(par_test@species_params$R_max, par_test@resource_params$kappa)


getError_Rmax(vary = Rmax_vary, params = par_test, dat = par_test@species_params$biomass_observed, data_type="biomass", timetorun = 100)

getError_erepro(vary = erepro_vary, params = par_test, dat = par_test@species_params$biomass_observed, data_type="biomass", timetorun = 100)

getError_Bio(vary = bio_vary, params = par_test, dat = par_test@species_params$biomass_observed, data_type="biomass", timetorun = 100)
```

```{r}
min(param_setup@species_params$R_max[])
max(param_setup@species_params$R_max[])
```


Optimise

```{r}
library(parallel)
#create a set of params for the optimisation process
params_optim <- par_test
params_optim <- setParams(params_optim)

#set up workers
noCores <- detectCores() - 2 # keep some spare cores
cl <- makeCluster(noCores, setup_timeout = 0.5)
setDefaultCluster(cl = cl)
clusterExport(cl, as.list(ls()))
clusterEvalQ(cl, {
  library(mizerExperimental)
  library(mizerMR)
  library(optimParallel)
})

optim_result <- optimParallel::optimParallel(par=Rmax_vary,getError_Rmax,params=params_optim, 
                                             dat = params@species_params$biomass_observed, 
                                             data_type="biomass", timetorun = 100, 
                                             method ="L-BFGS-B",
                                             lower=c(rep(1e-20,length(params@species_params$species))),
                                             upper= c(rep(10,length(params@species_params$species))),
                                             parallel=list(loginfo=TRUE, forward=TRUE))
stopCluster(cl)


```

```{r}
params_optim@species_params$R_max
# params_optim@species_params$erepro


optim_result$par[1:12]
# optim_result$par[13]
```

```{r}
params_optim_v2 <- params_optim
# now put these new Rmaxs
# optim values:
 params_optim_v2@species_params$R_max <- optim_result$par[1:12] # removed the 10^
 # params_optim@species_params$erepro <- optim_result$par[1:12]

 # params_optim@resource_params$kappa <- optim_result$par[13]
 
 # vary_optim <- optim_result$par
 # vary_optim[13] <- 1e3
 # 
 # getErrorBio2(vary = vary_optim,
 #              params_optim, dat = params_optim@species_params$biomass_observed)
 
 #check values ^^ have they gone to max/min etc.
 
params_optim_v2@species_params$max_lim <- 10
params_optim_v2@species_params$min_lim <- 1e-20
# 
params_optim_v2@species_params$R_max > params_optim_v2@species_params$min_lim
params_optim_v2@species_params$R_max < params_optim_v2@species_params$max_lim

```


```{r}
 params_optim_v2 <-setParams(params_optim_v2)
 sim_optim <- project(params_optim_v2, effort =0, t_max = 100)

 plot(sim_optim)
 plotlyBiomass(sim_optim)
 plotDiet(sim_optim)
```

Optimise again

```{r}
params_optim_v2@resource_params$kappa 
```

```{r}
params_optim_v3 <- steady(params_optim_v2)
```

```{r}
plotSpectra(params_optim_v3, power =2)
```

```{r}
params_optim_v4 <- tuneGrowth(params_optim_v3)
params_optim_v4 <- steady(params_optim_v4)
```

```{r}
params_optim_v4 <- readRDS("params_optim_v4.RDS")

sim_v3 <- project(params_optim_v4, effort =0, t_max = 100)
plotBiomassVsSpecies(params_optim_v4)
plotDiet(params_optim_v4)
plotBiomass(sim_v3)
plotlySpectra(sim_v3)
p1 <- plotSpectra(sim_v3)

# saveRDS(params_optim_v4, "params_optim_v4.RDS")
tiff(file="saving_spectra_plot.tiff",
width=6, height=4, units="in", res=500)
p1
dev.off()

```
If
AAD recommend that benthic resource is not meaningful for toothfish

Rmax can be justified by applying a density dependence relationship according to the trait based model (Anderson)
If finished with an optim run, could re-introduce density dependence that has been over written by steady()

We don't know what the level of density dependence should be set at. Can use yield curves, sensitivity to mortality...harvesting 

Pristine whale biomass pre-whaling. The 'base' model is actually the highly exploited system, can compare historical whaling time series (Chris Clements and Julia have this data)

Body size of whales responding to fishing 
- Follow 

Empricial size distribs of mammals

yield

seaaroundus

We are considering the whales within the Prydz Bay

Gear and catch ability for krill, ice fish and toothfish

Combine Stacey/Roshni model and get a total biomass


```{r}
params_optim_v4@species_params$erepro
params_optim_v5 <- tuneParams(params_optim_v4)
# params_optim_v5 <- tuneGrowth(params_optim_v4)

getReproductionLevel(params_optim_v4) # no density dependence for groups with 0

# Can try optimise with reproduction level

params_optim_v4@species_params$R_max
params_optim_v4@species_params$erepro


```
First ecosystem mizer model

```{r}
# library(parallel)
# #create a set of params for the optimisation process
# # param_optim_v2
# param_optim_v2 <- setParams(param_optim_v2)
# 
# #set up workers
# noCores <- detectCores() - 2 # keep some spare cores
# cl <- makeCluster(noCores, setup_timeout = 0.5)
# setDefaultCluster(cl = cl)
# clusterExport(cl, as.list(ls()))
# clusterEvalQ(cl, {
#   library(mizerExperimental)
#   library(mizerMR)
#   library(optimParallel)
# })
# 
# optim_result <- optimParallel::optimParallel(par=bio_vary,getError_Bio,params=params_optim, 
#                                              dat = params@species_params$biomass_observed, 
#                                              data_type="biomass", timetorun = 100, 
#                                              method ="L-BFGS-B",
#                                              lower=c(rep(1e-20,length(params@species_params$species)),1),
#                                              upper= c(rep(10,length(params@species_params$species)),1e+15),
#                                              parallel=list(loginfo=TRUE, forward=TRUE))
# stopCluster(cl)
```


## Add Benthic Resource Spectrum

```{r}
# Set benthos kappa same as plankton kappa
kappa_ben <- kappa
# Assume more shallow slope for benthos
lambda_ben <- 1.9
# Set maximum benthos size
w_max_ben <- 10
# Benthos starts at larger sizes, corresponding to about 1-2mm
w_min_ben <- 0.0001
```



Now we put all these resource parameters into a data frame.

```{r}
MFSO_resource_params_v2 <- data.frame(
    resource = c("pl", "ap", "bb"),
    lambda = c(lambda, lambda_ap, lambda_ben),
    kappa = c(kappa,  kappa_ap, kappa_ben),
    w_min = c(w_min, w_min_ap, w_min_ben),
    w_max = c(w_max,  w_max_ap, w_max_ben)
)

MFSO_resource_params_v2
```


```{r}
params_bb <- params_optim_v4
#set vectors of plankton and benthos availability for the model species 
plankton_avail <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.25)
ap_avail <- c(0, 0, 0, 0, 0, 0, 0, 0, 0.25, 0.75, 0.75, 0)
bb_avail <- c(0.1, 0, 0.25, 0.25, 0, 0.25, 0.25, 0.5, 0.25, 0, 0, 0)


#put them into corresponding columns of resource_interaction matrix
resource_interaction(params_bb)[, 1] <- plankton_avail
resource_interaction(params_bb)[, 2] <- ap_avail
resource_interaction(params_bb)[, 3] <- bb_avail

```


We can now update our model to use these resource parameters with

```{r}

params_optim_v5 <- setMultipleResources(params_optim_v4, MFSO_resource_params_v2)

resource_params(params_optim_v4) <- MFSO_resource_params_v2
```


```{r}
resource_interaction(params_optim_v4)
```



```{r}
#set vectors of plankton and benthos availability for the model species 
plankton_avail <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.25)
ap_avail <- c(0, 0, 0, 0, 0, 0, 0, 0, 0.25, 0.75, 0.75, 0)

#put them into corresponding columns of resource_interaction matrix
resource_interaction(params_guessed)[, 1] <- plankton_avail
resource_interaction(params_guessed)[, 2] <- ap_avail
```

Confirm it worked
```{r}
resource_interaction(params_guessed)
```





